class_docstr: MLP w/ GLU style gating
  See: https://arxiv.org/abs/1612.08083, https://arxiv.org/abs/2002.05202
code: def __init__(
      self,
      in_features,
      hidden_features=None,
      out_features=None,
      act_layer=nn.Sigmoid,
      norm_layer=None,
      bias=True,
      drop=0.0,
      use_conv=False,
      gate_last=True,
  ):
      super().__init__()
      out_features = out_features or in_features
      hidden_features = hidden_features or in_features
      assert hidden_features % 2 == 0
      bias = to_2tuple(bias)
      drop_probs = to_2tuple(drop)
      linear_layer = (
          partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear
      )
      self.chunk_dim = 1 if use_conv else -1
      self.gate_last = gate_last  # use second half of width for gate
  
      self.fc1 = linear_layer(
          in_features, hidden_features, bias=bias[0]
      )
      self.act = act_layer()
      self.drop1 = nn.Dropout(drop_probs[0])
      self.norm = (
          norm_layer(hidden_features // 2)
          if norm_layer is not None
          else nn.Identity()
      )
      self.fc2 = linear_layer(
          hidden_features // 2, out_features, bias=bias[1]
      )
      self.drop2 = nn.Dropout(drop_probs[1])
  
imports: from functools import partial
  from torch import nn
  from .grn import GlobalResponseNorm
  from .helpers import to_2tuple
label_desc: Use image node_id 1 to create a new GluMlp object from inherited base classes: nn with example: obj = GluMlp(in_features, hidden_features, out_features, act_layer, norm_layer, bias, drop, use_conv, gate_last)